Utilising AI in the legal assistance sectorTesting a role for Legal Information Institutes

Andrew MowbrayAustLII

University of Technology SydneySydney, Australia

andrew@austlii.edu.au

Philip ChungAustLII

University of New South WalesSydney, Australia

philip@austlii.edu.au

Graham GreenleafAustLII

University of New South WalesSydney, Australia

graham@austlii.edu.au

ABSTRACTThe use of artificial intelligence (AI) in law has again become ofgreat interest to lawyers and government. Legal InformationInstitutes (LIIs) have played a significant role in the provision oflegal information via the web. The concept of ‘free access to law’ isnot static, and its principles now require a LII response to therenewed prominence of AI, possibly to include improving andexpanding free access to legal advice.

This paper proposes, and proposes to test, one approach thatLIIs might take in the use of AI (specifically, ‘decision support’ or‘intelligent assistance’ (IA) technologies), an approach thatleverages the very large legal information assets that some LIIshave built over the past two decades. This approach focuses onhow LIIs can assist providers of free legal advice (the ‘legalassistance sector’) to serve their clients. We consider theconstraints that the requirement of ‘free’ imposes (on both thelegal assistance sector and on LIIs), including on what types of freelegal advice systems are sustainable, and what roles LIIs mayrealistically play in the development of such a ‘commons of freelegal advice’. We suggest guidelines for development of suchsystems. The AI-related services and tools that the AustralasianLegal Information Institute (AustLII) is providing (the ‘DataLex’platform) are outlined.

CCS CONCEPTS• Information systems → Expert systems; Wikis; • Appliedcomputing → Law;

KEYWORDSdecision-support systems, legal information institutes, legal assistance sector

1 INTRODUCTIONThe use of artificial i ntelligence (AI) i n l aw, i ncluding i n r elation to decision-support systems, has again become a matter of great interest to both the legal profession and to government. Theprevious wave of enthusiasm for, and investment in, ‘AI and law’ from the early 1980s to the mid-1990s was to a large extentsupplanted by the development of the World-Wide-Web and the

In: Proceedings of the First International Workshop on AI and Intelligent Assistance for Legal Professionals in the Digital Workplace (LegalAIIA 2019), held in conjunction with ICAIL 2019, June 17, 2019, Montréal, Québec, Canada.Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).Published at http://ceur-ws.org.

provision of legal information via the web. Legal InformationInstitutes (LIIs) and the Free Access to Law Movement (FALM),1played a very significant role in those developments [1]. Whatroles might LIIs play in this new AI-oriented environment?

The concept of ‘free access to law’ is not static, and has evolvedover the past quarter-century [2]. The principles of free access tolaw now require a LII response to the renewed prominence of AI-related developments in law, which could include improving andexpanding free access to legal advice, as part of ‘free access to law’,consistent with those of FALM’s Declaration of Free Access to Law[3]. ‘Freeing the law’ is a continuous process.

This paper proposes, and proposes to test, one approach thatLIIs might take in the use of AI (specifically, ‘decision support’ or‘intelligent assistance’ (IA) technologies), an approach thatleverages the very large legal information assets that some LIIshave built over the past two decades.2 This approach focuses onhow LIIs can assist providers of free legal advice (the ‘legalassistance sector’) to serve their clients. We consider theconstraints that the requirement of ‘free’ imposes (on both thelegal assistance sector and on LIIs), including on what types of freelegal advice systems are sustainable, and what roles LIIs mayrealistically play in the development of such a ‘commons of freelegal advice’. We suggest guidelines for development of suchsystems.

The Australasian Legal Information Institute (AustLII) isproviding AI-based services and tools (the ‘DataLex’ platform),which are described, including how they implement theseguidelines. A decision support system on rental housing law is tobe developed to implement, test and evaluate the above approach,with the DataLex platform being used by pro bono knowledge-base(KB) developers from a large law firm to develop the application,working in conjunction with a community legal centre that willutilise it, and a university research centre to evaluate the project.

2 DEGREES OF FREEDOM: TRAJECTORIES OFTHE DIGITIZATION OF EXPERTISE

The ‘Web 2.0’ context since about 2004 creates a very differentenvironment from the pre-1995 (pre-Internet, in popular usage)context of the first wave of ‘AI and law’ This context makes itmore feasible to talk about the collaborative development of freelegal advice services based on AI. The reasons include thesignificant roles that collaboration, in the form of FOSS (free andopen source software) and open content (exemplified by Creative1FALM website < http://www.falm.info/> – FALM has over 60 members.2Other LIIs which have built AI-based tools to complement their databases includeCanLII/Lexum (now merged).

LegalAIIA Workshop, ICAIL ’19, June 17, 2019, Montréal, Québec, Canada Mowbray, Chung and Greenleaf

Commons licensing and Wikipedia) have had on the developmentof the Internet; the much greater sophistication of interfaces; andthe possibilities of interaction between AI-based tools and hugeamounts of free access legal content.

We can distinguish three types of digitisation relevant to thegiving of professional legal advice: representation of informationused by experts; representation of expertise and its generalapplication; and application of expertise to individual situations.These categories overlap in reality, but these distinctions enable usto consider more precisely [4] how likely is it that each categorywill become part of a ‘commons of legal expertise’.

(I) Representing Expert Domain Information. ‘Raw’ (primary)information used by experts is the most likely aspect of expertiseboth to be digitised and to become part of the commons. Databasesof primary information essential to legal professionals (legislation,treaties, court decisions etc) are already substantially digitised andavailable online, and with increasing utility (eg smarter retrievalsystems, and smarter data structures). In many countriessubstantial amounts are available as commons, at least for freeaccess and often as open content, usually via government sources.In a few dozen countries such as Australia, free access ‘legalinformation institutes’ (‘LIIs’) aggregate this data and add value toit, making it a resource used by professionals and the generalpublic alike. Even though some primary information is onlyavailable commercially, in less than 25 years since the start ofwidespread availability of such data via the web, the increase infree availability is extraordinary, and is tending toward acomprehensive commons.

(II) Representing Expertise in General Form. When professionalexpertise is represented (or embodied or reified) this is usually in ageneralised form which may or may not be applicable to anindividual situation where expertise is needed, because of theenormous variation of individual situations which may arise. It isup to the reader (usually the correct term) to apply the expertise tothe individual situation. Legal professionals represented theirexpertise in many ways prior to the Internet — in textbooks,journal articles, encyclopedias, and in very significant, but moremundane, forms such as citators and checklists (often assupervisors of non-professionals). The economics of publishingmeant that such reification of expertise could rarely be provided asa commons, and instead it usually became an economic asset of acommercial publisher and an author.

The Internet changes some but not all of these factors. Expertiseremains a very valuable asset which many professionals arereluctant to embody in any form of commons. However, the lastquarter century has revealed revolutionary potential which is onlybecoming apparent through the accretion of successes, includingfree access repositories of current scholarship, archives ofpublished journals, changing academic funding requirements,peer-reviewed free content, viral licensing; crowd-sourcing;collaborative editing by closed professional groups; and automatedsubstitutions for expertise. A ‘closed wiki’ model, where contentmay only be edited by professionals may be most suitable for law,because of its emphasis on authority. Successful commonsexamples developed by AustLII including multi-author guidebooks[5], and automated citators performing to professional levels [6].

The results demonstrate that it is becoming viable for professionalsto control the representation of their own expertise, as a commons.

(III) Applying Expertise to Individual Situations. It is the thirdcategory, the application of expertise to individual situations (theproblems of individual clients) via programs, which is seen widelyas a major threat to the future of professionals and professions [4].At present, the number of convincing examples and theircommercial viability do not make it inevitable that there will begeneralised dire results for professions. To understand the likelyimplications, it is necessary to distinguish at least three types ofthe programmatic applications of legal expertise: human expertiseembodied in knowledge-bases which interact with programs;embedded knowledge in artifacts; and machine-generatedexpertise. The first is most relevant to the provision of legal advice.The question is whether, in those areas where legal expertise canbe effectively captured in knowledge-bases to be used indecision-support systems, can they be developed as a commons, oronly as commercial products?

3 AN ALTERNATIVE FUTURE: A COMMONSOF LEGAL EXPERTISE

Although there is as yet no obvious tendency toward commons inrelation to the three categories of software-based application ofexpertise to individual cases, we argue that this can be encouragedto develop. Tools for knowledge engineering and for creatingmachine-generated expertise are available as FOSS and are of highquality, but the communities of users necessary to developapplications (similar to the FOSS or Wikipedia communities) havenot yet developed. We argue that such collaborative alternativecould arise primarily from those organisations that seek to providefree legal advice, and be driven largely by their needs, but couldexpand to involve other participants in the legal profession.

3.1 The providers and constraints of free legaladvice

There are many situations where, at least in a country likeAustralia, our social expectation is that legal advice be providedwithout cost to the public, whether as consumers, citizens or(sometimes) litigants. The organisations most likely to be involvedin providing such free3 legal advice are quite diverse, and includegovernment legal aid providers, community legal centres,government and community consumer advice centres, specialistNGOs in law-related areas, government agencies giving advicerelevant to their functions, and ‘chamber magistrates’ incourthouses. The legal profession, through state and regional LawSocieties and advice centres they provide, and through theextensive pro bono schemes, also contributes. University lawschools, through their involvement in community legal centresand internships in other organisations, are potential sources ofcontributors who often have high computing skills. Bodiesassisting the legal profession as a whole to avoid liability problems,such as some legal insurers, might also wish to participate.

3‘Free’ entails ‘free from surveillance’, a test which some commercial providers ofostensibly ‘free’ services will fail: see [2].

Utilising AI in the legal assistance sector LegalAIIA Workshop, ICAIL ’19, June 17, 2019, Montréal, Québec, Canada

A common factor for most of these providers of free legal adviceis that, if they choose to develop AI-related tools to assist their work,theywill usually have to do sowithin very constrained developmentand maintenance budgets for software or applications. They arenot in a position to pass on such costs to clients, or to purchasersof applications. Government or other grants for such developmentsmay provide up-front development costs (at least while the hypecycle for AI is rising) but will rarely cover ongoing maintenance forapplications as the law changes, or technical issues arise. Bringingin out-of-house consultants on specialised software problems, oras ‘knowledge engineers’ in relation to particular legal domains, islikely to be very expensive. It is therefore a reasonable assumptionthat, at least in the medium to long term, providers of free legaladvice will have to work within significant financial constraints thatare more severe than those experienced by commercial providers.

The implications of these constraints – limited institutional rangeof providers, and limited financial resources – affect the types oflegal advisory systems that it is practical for this sector to developand support.

3.2 Free legal advisory systems: Guidelines forsustainability

Wehave previously set out and justified our views onwhat approachto the use of AI tools is most likely to be of value to a free legaladvice service ([7], at 3.1-3.16). These guidelines are based on theassumptions discussed above of the likely limited financial andpersonnel resources of such a service, and on our own lengthyexperience with the DataLex project. They are implemented in theDataLex platform discussed in the following section.

First, the ‘AI and law’ systems that such a service could beexpected to find useful are those that justify their answers at leastin part in terms of the formal sources of law. These constraints willmean that only some types of ‘AI and law’ tools are suitable totheir needs.

Second, looked at from the user perspective, which could bethat of an employee of a free legal advice service, or perhaps oneof its clients, what counts as a useful level of legal expertise isrelative. A system may be valuable to a class of users even thoughit has a relatively low point at which it admits that a problem isbeyond its expertise, and it may serve as a method of triage. In anyevent, it is not realistic to try to build legal expert systems thatencapsulate all the knowledge necessary to answer user problems.The more realistic aim is to build decision support systems, inthe use of which the program and the user in effect pool theirknowledge/expertise to resolve a problem. Expertise can and shouldbe represented and utilised by programs in many ways. This meansthe knowledge-based system (the knowledge representation andthe program) should not be ‘closed’: it must be integrated withtext retrieval, hypertext and other tools which allow and assist theuser to obtain access to whatever source materials are necessary toanswer the parts of a problem dependent on the user’s expertise.The result is an integrated decision-support system.

Third, looked at from the developer perspective, the keycontextual factor is that user-organisations such as free legaladvice services, will probably need to both develop and maintaintheir own knowledge-bases, as the only available domain experts.

The systems which non-technical legal domain experts are mostlikely to be able to develop and maintain are those which representlegal knowledge in a way which has a reasonably high level ofisomorphism (one-to-one correspondence) with the legal sourceson which it is based, where the representation is reasonably closeto natural language, and where it is not necessary to prescribe theorder(s) of the procedural steps necessary to reach a solution to aproblem, but only to declare what legal knowledge is available,and leave it to the system to undertake the steps to apply thatknowledge.

Fourth, correctly choosing the type of problemwhere ‘AI and law’techniques are most likely to be appropriate is essential. Problemareas based on legislation, or procedural steps, and where there iscomplexity, will probably give the best results. Problems involvingmultiple instances of one factor increase logical difficulty. If it isadministratively possible to have multiple organisations collaborateto build and maintain a legal knowledge base, this may increasesustainability.

3.3 The likely roles of LIIsFifth, we conclude that free access legal information institutes (LIIs)are unlikely to be the builders of legal knowledge-bases in particularlegal domains, because they do not have the necessary in-houseexpertise in legal subject domains. They have neither the client-basethat provides a continuing need for such expertise, nor the fundsto retain such expertise from outside (at least not on a continuingbasis, beyond an initial grant). As a result, LIIs are much more likelyto be the providers of tools by which such knowledge-bases arebuilt, the free access legal infrastructure within which they are built,and education and support for those organisations that use theirtools and services to build and maintain subject-area applications.4In light of that conclusion, we now move to the tools and servicesthat AustLII is building.

4 AI IN A LII: AUSTLII’S DATALEXIMPLEMENTATION

The Australasian Legal Information Institute (AustLII), through itsDataLex project ([7] at 2, [8]) is developing tools and infrastructureso as to implement the above ‘sustainable legal advisory systems’approach to AI and law in the context of a LII. This platform includesfive main elements, rectangles in the following diagram (Figure 1).The features of each are then summarised.

4.1 The DataLex inferencing softwareThe DataLex inferencing software5 primarily carries out rule-basedreasoning. It has the following key features:

• Support for backward-chaining and forward-chaining rule-based reasoning. Rules are expressed in a declarative form.

• Rule-based reasoning is supplemented by procedural code,where procedural steps in reasoning are needed.

4Length constraints preclude a survey of what projects other LIIs are undertaking.5The DataLex inferencing software was originally written by Andrew Mowbray, asy-sh (‘y-shell’), with subsequent further layers by various authors including SimonCant and Philip Chung, to enable web-based operation.

LegalAIIA Workshop, ICAIL ’19, June 17, 2019, Montréal, Québec, Canada Mowbray, Chung and Greenleaf

Figure 1: Components of AustLII’s DataLex legalinferencing platform.

• Rule based reasoning is also supplemented by example-based(or ‘case-based’) reasoning,6 where needed.

• Rules of any degree of complexity may be written, usingpropositional logic.

• A quasi-natural-language knowledge-base syntax (ie oneresembling English as far as is possible) is used to declarerules (and examples).

• There is no separate coding of questions, explanations andreports, because they are all generated automatically fromthe declared rules, in dialogues generated ‘on the fly’ whenthe system is in operation. This default operation can becustomised where special circumstances require.

• Isomorphic (one-to-one) relationships between theknowledge-base and legislation is facilitated, and assists indebugging and updating.

• The previous three elements allow easier development,debugging and maintenance by domain experts (lawyers),without involvement by software experts or ‘knowledgeengineers’.

• Collaborative development of larger applications acrossdistributed knowledge-bases is supported.

An extract from the ElectKB knowledge-base [10] is shown inFigure 2.

Figure 2: Extract of a DataLex knowledge-base (or rulebase).

6PANNDA (Precedent Analysis by Nearest-Neighbour Discriminant Analysis); see [9]for details about the FINDER (finders’ cases) application of PANNDA.

4.2 The AustLII Communities environment –integrating AI with a LII

The AustLII Communities environment is used to linkautomatically both knowledge-bases under development, andadvisory systems when in operation, with all of the free accesslegal materials provided by a LII. The hypertext links in the aboveknowledge-base extract are inserted automatically, using AustLII’sfindacts software, into the knowledge-base as it is written andsaved. Further examples of links from applications in operation aregiven below.

4.3 The DataLex knowledge-base developmenttools

The DataLex development tools [11] are situated within the AustLIICommunities infrastructure. They use a familiar wiki-like editinginterface for development and maintenance of knowledge-bases(KBs). Development is within a closed wiki environment.

4.4 The DataLex user interfaceThe DataLex user interface uses the DataLex software andknowledge-bases, the linkages provided by the Communitiesenvironment, and user input, to provide legal advisory systems inoperation.

From Figure 3, it can be seen that some of the features of theinterface include:

• Questions, Facts, Conclusions, and Reports are all generatedfrom the knowledge-base and user-provided facts, inunderstandable form, and are available on screen at alltimes.

• Facts can be deleted (‘Forget?’), and questions thenre-asked; Conclusions can be explained (‘How?’); andreasons for Questions requested (‘Why?’), generated in thesame manner.

• The system also uses all information available to it, from theknowledge-base and user-supplied facts, to suggest otherrelevant Related Materials.

As the consultation continues, conclusions are shown on theright-hand side. Selection of a numbered conclusion results in a‘How’ explanation of that conclusion being presented, as shown inFigure 4.

At the end of the consultation, a composite explanation of thefinal result, and of all the steps necessary for it to be reached,is displayed and may be exported to word processing or otherprograms for use.

4.5 The LawCite citator and SINO searchengine – updating and expanding advice

SINO is the open source search engine, developed by AustLII [12],used to operate AustLII and other LIIs. The LawCite citator [6] is anautomated international citator for case law and legal scholarship,accessible to end-users free of any user charges. It is developedand maintained by AustLII in conjunction with a consortium ofparticipating legal information institutes (LIIs). LawCite currentlycontains index records of the citation histories of over 5.7 millioncases, law journal articles, law reform documents and treaties, going

Utilising AI in the legal assistance sector LegalAIIA Workshop, ICAIL ’19, June 17, 2019, Montréal, Québec, Canada

Figure 3: DataLex user interface features: Consultation, Facts, Conclusions, Related Materials.

Figure 4: DataLex ‘How’ explanation during consultation.

back to the 1300s. It includes citation records in significant numbersfrom court decisions in 75 countries. It is integrated fully into theoperations of AustLII and other LIIs that use it. The technical detailsof LawCite are explained elsewhere [13].

The significance of both LawCite and SINO within the DataLexproject is that they provide a means of (in effect) expanding thescope of a knowledge-base by providing users with access toknowledge which is not yet encoded within the knowledge-base.Examples are as follows, from the ElectKB knowledge-base [10]concerning disqualification for eligibility for election to theAustralian federal Parliament:

(a) Wherever the term ‘foreign power’ appears in a consultationdialogue, it does so as a hypertext link which triggers asearch over AustLII for all occurrences of ‘foreign power’ inthe context of s 44 of the Australian Constitution (Figure 5).

Figure 5: Embedded search link to ‘foreign power’ duringDataLex consultation.

The user is then given a list of cases, journal article etc,ranked in default by likely order of relevance, to enable themto determine the correct answer to the question (Figure 6).

(b) Wherever a citation for a case appears in a dialogue, it willbe linked automatically to the text of the case (where it is aneutral citation), with a further link to the LawCite record,as in the following example (Figure 7).

LegalAIIA Workshop, ICAIL ’19, June 17, 2019, Montréal, Québec, Canada Mowbray, Chung and Greenleaf

Figure 6: Search results from embedded search link.

Figure 7: DataLex automated links to case law references.

The user is able to note from the LawCite citation recordwhether that case has been considered by other casessubsequent to the knowledge-base being written, and tocheck for any resulting changes to the law. Noknowledge-base can be updated as frequently as the lawmight change,7 and this is particularly so when they aresubject to the constraints discussed in part 3. For example,the LawCite record for this case alerts the user to recentcases considering Sykes v Cleary, that may not yet be takenaccount of in the knowledge-base (Figure 8).

It should be clear from these examples that updating a legalknowledge-base through links and searches requires access to thecase and legislation content of a whole legal system, updatedcontinuously. For providers of free legal advice, the most feasiblesource of such information is a free access legal informationinstitute (LII).

5 A COLLABORATIVE PROJECT FOR LEGALASSISTANCE LAWYERS AND THEIRCLIENTS

The next steps of this project will be (once funding is secured) totest the approach advocated in this paper will utilise AustLII’sDataLex platform to demonstrate how pro bono legal assistanceprograms can result in development and use of shared AI-basedlegal resources (‘apps’), integrated fully with AustLII’s databases.The application to be developed concerns NSW tenancy law(particularly the Residential Tenancies Act 2010), an area of

7A knowledge-base maintained by a legislature is a partial exception (would notinclude case-law changes). Automated programmatic updating is a formidable task,and unlikely.

significant unmet legal need, as documented in the Redfern LegalCentre (RLC) response ([14]) to [15]. There are four projectpartners:

(1) AustLII is to provide (and further develop) the DataLexplatform, including the inferencing software, Communitiesenvironment, and underlying legal databases on AustLII,plus software developer skills and knowledge, and projectmanagement.

(2) The Pro Bono & Community Impact program of King andWood Mallesons (KWM)8 is to provide lawyers under its probono program who will work with RLC to build a knowledge-base (‘the application’) in the tenancy law area, using theDataLex platform.

(3) Redfern Legal Centre (RLC)9 is to provide legal staff andvolunteers working in its tenancy advice practice, to utilisethe application in its advisory work. It will provide feedbackabout both the application and the platform, so that theycan be improved iteratively, to KWM lawyers and toAustLII developers. Once the application is tested by RLC, itand AustLII will decide whether a version of it can also beprovided for direct use by the public, via AustLII andthrough links from RLC’s website.

(4) The Australian Pro Bono Centre (APBC)10 at UNSW is toprovide an independent evaluation of the development ofthe application, and the outcome of the trial of its use. Basedon this evaluation it will provide advice to AustLII to assistAustLII to develop a methodology by which the DataLexplatform can be more widely applied in the pro bono field tosupport free access legal advice to the community.

The co-ordinating body for NSW Community Legal Centreshas also agreed to examine how it can both assist legal centressharing applications developed using this approach, and assist inthe identification of which bodies in the legal assistance sector

8https://www.kwm.com/en/au/about-us/corporate-responsibility/pro-bono9https://rlc.org.au/10https://www.probonocentre.org.au/

Utilising AI in the legal assistance sector LegalAIIA Workshop, ICAIL ’19, June 17, 2019, Montréal, Québec, Canada

Figure 8: LawCite records for Sykes v Cleary.

would be most likely to participate in the development of newapplications.

6 CONCLUSIONS – WHEN IS AI FEASIBLEFOR THE LEGAL ASSISTANCE SECTOR?

In this paper we have identified why providers of free legal adviceare likely to face significant constraints on the resources availableto them to develop and maintain AI-based legal advisory systems,and the implications this has for the types of systems they aremost likely to use. We have proposed guidelines which will enabledevelopment which is sustainable by the organisations likely to beproviding such advice, and which will contribute to an expandingcommons of legal expertise embodied in AI-based tools.

We have set out the approach that AustLII, through its DataLexplatform, is taking to facilitate the development of such systems, andhow the DataLex approach allows implementation of the guidelinesfor sustainable legal AI that we have proposed. We have outlineda collaborative project to build, test and evaluate both the generalapproach, and an application on rental housing law.

REFERENCESAll URLs were accessed 18 April 2019.[1] Graham Greenleaf, Philip Chung, and Andrew Mowbray. Update: Legal

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[2] Graham Greenleaf, Andrew Mowbray, and Philip Chung. The meaning of ’freeaccess to legal information’: A twenty year evolution. Journal of Open Access

to Law (JOAL), 1:1, 2013. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2158868.

[3] FALM. Declaration on Free Access to Law, 2002-. http://www.falm.info/declaration/.

[4] Graham Greenleaf. Review essay – technology and the professions: Utopian anddystopian futures. UNSW Law Journal, 40(1):302–321, 2017. https://ssrn.com/abstract=2973244 or [2017] UNSWLawJl 12.

[5] Melinda Schroeder (ed) and 70 authors. Northern Territory Law Handbook, 26July 2016. http://austlii.community/wiki/NTLawHbk/NTLawHandbook.

[6] Andrew Mowbray. LawCite Citator, 2008-. http://www.austlii.edu.au/lawcite/.[7] Graham Greenleaf, Andrew Mowbray, and Philip Chung. Building sustainable

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[8] Graham Greenleaf, Andrew Mowbray, and Philip Chung. The Datalex Project:History and Bibliography, 2018. UNSW Law Research Paper No 18-4. https://ssrn.com/abstract=3095897 or [2018] UNSWLRS 4.

[9] Alan Tyree, Graham Greenleaf, and Andrew Mowbray. Generating legalarguments. Knowledge-Based Systems, 2(1):46–51, 1989. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2988931.

[10] Andrew Mowbray. ElectKB knowledge-base, February 2019. http://austlii.community/wiki/DataLex/ElectKB.

[11] AustLII. DataLex development tools, 2019-. http://austlii.community/wiki/DataLex.

[12] Andrew Mowbray. SINO free text search engine, 1996-. AustLII Open sourcesoftware (Unix/C) http://www.austlii.edu.au/techlib/software/sino/.

[13] AndrewMowbray, Philip Chung, andGrahamGreenleaf. A free access, automatedlaw citator with international scope: The LawCite project. European Journal ofLaw and Technology (EJLT), 7(3), 2016. pre-print version at https://ssrn.com/abstract=2768104 or [2016] UNSWLRS 32.

[14] Redfern Legal Centre. Submission: Statutory Review of the Residential TenanciesAct 2010, 12 February 2016. https://rlc.org.au/sites/default/files/attachments/RLC%20submission%20to%20RTA%20Review.pdf.

[15] Department of Fair Trading (NSW). Discussion Paper: StatutoryReview of the Residential Tenancies Act 2010, October 2015. https://www.fairtrading.nsw.gov.au/__data/assets/pdf_file/0010/398287/Residential_tenancies_discussion_paper.pdf.